Overview

Dataset statistics

Number of variables32
Number of observations300000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory345.9 MiB
Average record size in memory1.2 KiB

Variable types

Numeric12
Categorical20

Warnings

cat5 has a high cardinality: 84 distinct values High cardinality
cat7 has a high cardinality: 51 distinct values High cardinality
cat8 has a high cardinality: 61 distinct values High cardinality
cat10 has a high cardinality: 299 distinct values High cardinality
id is uniformly distributed Uniform
id has unique values Unique

Reproduction

Analysis started2021-03-07 23:29:59.480531
Analysis finished2021-03-07 23:33:55.665779
Duration3 minutes and 56.19 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct300000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250183.4257
Minimum0
Maximum499999
Zeros1
Zeros (%)< 0.1%
Memory size2.3 MiB
2021-03-07T20:33:56.076219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25201.95
Q1125399.75
median250192.5
Q3375246.25
95-th percentile475017.05
Maximum499999
Range499999
Interquartile range (IQR)249846.5

Descriptive statistics

Standard deviation144320.3348
Coefficient of variation (CV)0.5768580969
Kurtosis-1.199830725
Mean250183.4257
Median Absolute Deviation (MAD)124931
Skewness-0.001836243566
Sum7.505502771 × 1010
Variance2.082835904 × 1010
MonotocityStrictly increasing
2021-03-07T20:33:56.414524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
4102691
 
< 0.1%
4389151
 
< 0.1%
4327741
 
< 0.1%
4307271
 
< 0.1%
4532561
 
< 0.1%
4573541
 
< 0.1%
4553071
 
< 0.1%
4450681
 
< 0.1%
4491661
 
< 0.1%
Other values (299990)299990
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
71
< 0.1%
101
< 0.1%
121
< 0.1%
131
< 0.1%
141
< 0.1%
ValueCountFrequency (%)
4999991
< 0.1%
4999971
< 0.1%
4999961
< 0.1%
4999951
< 0.1%
4999931
< 0.1%
4999921
< 0.1%
4999911
< 0.1%
4999901
< 0.1%
4999891
< 0.1%
4999881
< 0.1%

cat0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
A
223525 
B
76475 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
A223525
74.5%
B76475
 
25.5%
2021-03-07T20:33:57.505365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T20:33:57.737199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a223525
74.5%
b76475
 
25.5%

Most occurring characters

ValueCountFrequency (%)
A223525
74.5%
B76475
 
25.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
A223525
74.5%
B76475
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
A223525
74.5%
B76475
 
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
A223525
74.5%
B76475
 
25.5%

cat1
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
I
90809 
F
43818 
K
41870 
L
31891 
H
17257 
Other values (10)
74355 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowK
4th rowK
5th rowI
ValueCountFrequency (%)
I90809
30.3%
F43818
14.6%
K41870
14.0%
L31891
 
10.6%
H17257
 
5.8%
N13231
 
4.4%
M11354
 
3.8%
G11248
 
3.7%
A10547
 
3.5%
J10036
 
3.3%
Other values (5)17939
 
6.0%
2021-03-07T20:33:58.424908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i90809
30.3%
f43818
14.6%
k41870
14.0%
l31891
 
10.6%
h17257
 
5.8%
n13231
 
4.4%
m11354
 
3.8%
g11248
 
3.7%
a10547
 
3.5%
j10036
 
3.3%
Other values (5)17939
 
6.0%

Most occurring characters

ValueCountFrequency (%)
I90809
30.3%
F43818
14.6%
K41870
14.0%
L31891
 
10.6%
H17257
 
5.8%
N13231
 
4.4%
M11354
 
3.8%
G11248
 
3.7%
A10547
 
3.5%
J10036
 
3.3%
Other values (5)17939
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
I90809
30.3%
F43818
14.6%
K41870
14.0%
L31891
 
10.6%
H17257
 
5.8%
N13231
 
4.4%
M11354
 
3.8%
G11248
 
3.7%
A10547
 
3.5%
J10036
 
3.3%
Other values (5)17939
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
I90809
30.3%
F43818
14.6%
K41870
14.0%
L31891
 
10.6%
H17257
 
5.8%
N13231
 
4.4%
M11354
 
3.8%
G11248
 
3.7%
A10547
 
3.5%
J10036
 
3.3%
Other values (5)17939
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
I90809
30.3%
F43818
14.6%
K41870
14.0%
L31891
 
10.6%
H17257
 
5.8%
N13231
 
4.4%
M11354
 
3.8%
G11248
 
3.7%
A10547
 
3.5%
J10036
 
3.3%
Other values (5)17939
 
6.0%

cat2
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
A
168694 
C
38875 
D
22720 
G
18225 
Q
 
10901
Other values (14)
40585 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowG
ValueCountFrequency (%)
A168694
56.2%
C38875
 
13.0%
D22720
 
7.6%
G18225
 
6.1%
Q10901
 
3.6%
F9877
 
3.3%
J9102
 
3.0%
M8068
 
2.7%
I5287
 
1.8%
L3997
 
1.3%
Other values (9)4254
 
1.4%
2021-03-07T20:33:59.324140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a168694
56.2%
c38875
 
13.0%
d22720
 
7.6%
g18225
 
6.1%
q10901
 
3.6%
f9877
 
3.3%
j9102
 
3.0%
m8068
 
2.7%
i5287
 
1.8%
l3997
 
1.3%
Other values (9)4254
 
1.4%

Most occurring characters

ValueCountFrequency (%)
A168694
56.2%
C38875
 
13.0%
D22720
 
7.6%
G18225
 
6.1%
Q10901
 
3.6%
F9877
 
3.3%
J9102
 
3.0%
M8068
 
2.7%
I5287
 
1.8%
L3997
 
1.3%
Other values (9)4254
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
A168694
56.2%
C38875
 
13.0%
D22720
 
7.6%
G18225
 
6.1%
Q10901
 
3.6%
F9877
 
3.3%
J9102
 
3.0%
M8068
 
2.7%
I5287
 
1.8%
L3997
 
1.3%
Other values (9)4254
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
A168694
56.2%
C38875
 
13.0%
D22720
 
7.6%
G18225
 
6.1%
Q10901
 
3.6%
F9877
 
3.3%
J9102
 
3.0%
M8068
 
2.7%
I5287
 
1.8%
L3997
 
1.3%
Other values (9)4254
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
A168694
56.2%
C38875
 
13.0%
D22720
 
7.6%
G18225
 
6.1%
Q10901
 
3.6%
F9877
 
3.3%
J9102
 
3.0%
M8068
 
2.7%
I5287
 
1.8%
L3997
 
1.3%
Other values (9)4254
 
1.4%

cat3
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
A
187251 
B
79951 
C
 
15957
D
 
8676
E
 
3318
Other values (8)
 
4847

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowA
4th rowC
5th rowB
ValueCountFrequency (%)
A187251
62.4%
B79951
26.7%
C15957
 
5.3%
D8676
 
2.9%
E3318
 
1.1%
F2489
 
0.8%
K846
 
0.3%
G372
 
0.1%
L292
 
0.1%
J286
 
0.1%
Other values (3)562
 
0.2%
2021-03-07T20:34:00.150158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a187251
62.4%
b79951
26.7%
c15957
 
5.3%
d8676
 
2.9%
e3318
 
1.1%
f2489
 
0.8%
k846
 
0.3%
g372
 
0.1%
l292
 
0.1%
j286
 
0.1%
Other values (3)562
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A187251
62.4%
B79951
26.7%
C15957
 
5.3%
D8676
 
2.9%
E3318
 
1.1%
F2489
 
0.8%
K846
 
0.3%
G372
 
0.1%
L292
 
0.1%
J286
 
0.1%
Other values (3)562
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
A187251
62.4%
B79951
26.7%
C15957
 
5.3%
D8676
 
2.9%
E3318
 
1.1%
F2489
 
0.8%
K846
 
0.3%
G372
 
0.1%
L292
 
0.1%
J286
 
0.1%
Other values (3)562
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
A187251
62.4%
B79951
26.7%
C15957
 
5.3%
D8676
 
2.9%
E3318
 
1.1%
F2489
 
0.8%
K846
 
0.3%
G372
 
0.1%
L292
 
0.1%
J286
 
0.1%
Other values (3)562
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
A187251
62.4%
B79951
26.7%
C15957
 
5.3%
D8676
 
2.9%
E3318
 
1.1%
F2489
 
0.8%
K846
 
0.3%
G372
 
0.1%
L292
 
0.1%
J286
 
0.1%
Other values (3)562
 
0.2%

cat4
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
E
129385 
F
76678 
G
30754 
D
27919 
H
23388 
Other values (15)
 
11876

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowE
3rd rowE
4th rowE
5th rowE
ValueCountFrequency (%)
E129385
43.1%
F76678
25.6%
G30754
 
10.3%
D27919
 
9.3%
H23388
 
7.8%
J4307
 
1.4%
I3241
 
1.1%
K1481
 
0.5%
M547
 
0.2%
C506
 
0.2%
Other values (10)1794
 
0.6%
2021-03-07T20:34:00.618147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e129385
43.1%
f76678
25.6%
g30754
 
10.3%
d27919
 
9.3%
h23388
 
7.8%
j4307
 
1.4%
i3241
 
1.1%
k1481
 
0.5%
m547
 
0.2%
c506
 
0.2%
Other values (10)1794
 
0.6%

Most occurring characters

ValueCountFrequency (%)
E129385
43.1%
F76678
25.6%
G30754
 
10.3%
D27919
 
9.3%
H23388
 
7.8%
J4307
 
1.4%
I3241
 
1.1%
K1481
 
0.5%
M547
 
0.2%
C506
 
0.2%
Other values (10)1794
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
E129385
43.1%
F76678
25.6%
G30754
 
10.3%
D27919
 
9.3%
H23388
 
7.8%
J4307
 
1.4%
I3241
 
1.1%
K1481
 
0.5%
M547
 
0.2%
C506
 
0.2%
Other values (10)1794
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
E129385
43.1%
F76678
25.6%
G30754
 
10.3%
D27919
 
9.3%
H23388
 
7.8%
J4307
 
1.4%
I3241
 
1.1%
K1481
 
0.5%
M547
 
0.2%
C506
 
0.2%
Other values (10)1794
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
E129385
43.1%
F76678
25.6%
G30754
 
10.3%
D27919
 
9.3%
H23388
 
7.8%
J4307
 
1.4%
I3241
 
1.1%
K1481
 
0.5%
M547
 
0.2%
C506
 
0.2%
Other values (10)1794
 
0.6%

cat5
Categorical

HIGH CARDINALITY

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.9 MiB
BI
238563 
AB
41639 
BU
 
6740
K
 
2713
G
 
683
Other values (79)
 
9662

Length

Max length2
Median length2
Mean length1.97923
Min length1

Characters and Unicode

Total characters593769
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBI
2nd rowBI
3rd rowBI
4th rowBI
5th rowBI
ValueCountFrequency (%)
BI238563
79.5%
AB41639
 
13.9%
BU6740
 
2.2%
K2713
 
0.9%
G683
 
0.2%
BQ483
 
0.2%
N447
 
0.1%
CL336
 
0.1%
AL272
 
0.1%
BO239
 
0.1%
Other values (74)7885
 
2.6%
2021-03-07T20:34:01.055183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bi238563
79.5%
ab41639
 
13.9%
bu6740
 
2.2%
k2713
 
0.9%
g683
 
0.2%
bq483
 
0.2%
n447
 
0.1%
cl336
 
0.1%
al272
 
0.1%
bo239
 
0.1%
Other values (74)7885
 
2.6%

Most occurring characters

ValueCountFrequency (%)
B289880
48.8%
I239023
40.3%
A44777
 
7.5%
U6915
 
1.2%
K2998
 
0.5%
C1781
 
0.3%
G938
 
0.2%
L896
 
0.2%
Q757
 
0.1%
N582
 
0.1%
Other values (16)5222
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter593769
100.0%

Most frequent character per category

ValueCountFrequency (%)
B289880
48.8%
I239023
40.3%
A44777
 
7.5%
U6915
 
1.2%
K2998
 
0.5%
C1781
 
0.3%
G938
 
0.2%
L896
 
0.2%
Q757
 
0.1%
N582
 
0.1%
Other values (16)5222
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin593769
100.0%

Most frequent character per script

ValueCountFrequency (%)
B289880
48.8%
I239023
40.3%
A44777
 
7.5%
U6915
 
1.2%
K2998
 
0.5%
C1781
 
0.3%
G938
 
0.2%
L896
 
0.2%
Q757
 
0.1%
N582
 
0.1%
Other values (16)5222
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII593769
100.0%

Most frequent character per block

ValueCountFrequency (%)
B289880
48.8%
I239023
40.3%
A44777
 
7.5%
U6915
 
1.2%
K2998
 
0.5%
C1781
 
0.3%
G938
 
0.2%
L896
 
0.2%
Q757
 
0.1%
N582
 
0.1%
Other values (16)5222
 
0.9%

cat6
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
A
187896 
C
71427 
E
 
16581
G
 
11198
I
 
6648
Other values (11)
 
6250

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowK
3rd rowA
4th rowA
5th rowC
ValueCountFrequency (%)
A187896
62.6%
C71427
 
23.8%
E16581
 
5.5%
G11198
 
3.7%
I6648
 
2.2%
M2182
 
0.7%
K1552
 
0.5%
O673
 
0.2%
S583
 
0.2%
F312
 
0.1%
Other values (6)948
 
0.3%
2021-03-07T20:34:01.439816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a187896
62.6%
c71427
 
23.8%
e16581
 
5.5%
g11198
 
3.7%
i6648
 
2.2%
m2182
 
0.7%
k1552
 
0.5%
o673
 
0.2%
s583
 
0.2%
f312
 
0.1%
Other values (6)948
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A187896
62.6%
C71427
 
23.8%
E16581
 
5.5%
G11198
 
3.7%
I6648
 
2.2%
M2182
 
0.7%
K1552
 
0.5%
O673
 
0.2%
S583
 
0.2%
F312
 
0.1%
Other values (6)948
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
A187896
62.6%
C71427
 
23.8%
E16581
 
5.5%
G11198
 
3.7%
I6648
 
2.2%
M2182
 
0.7%
K1552
 
0.5%
O673
 
0.2%
S583
 
0.2%
F312
 
0.1%
Other values (6)948
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
A187896
62.6%
C71427
 
23.8%
E16581
 
5.5%
G11198
 
3.7%
I6648
 
2.2%
M2182
 
0.7%
K1552
 
0.5%
O673
 
0.2%
S583
 
0.2%
F312
 
0.1%
Other values (6)948
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
A187896
62.6%
C71427
 
23.8%
E16581
 
5.5%
G11198
 
3.7%
I6648
 
2.2%
M2182
 
0.7%
K1552
 
0.5%
O673
 
0.2%
S583
 
0.2%
F312
 
0.1%
Other values (6)948
 
0.3%

cat7
Categorical

HIGH CARDINALITY

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
AH
45818 
E
39601 
AS
25326 
J
16135 
AN
16097 
Other values (46)
157023 

Length

Max length2
Median length2
Mean length1.511103333
Min length1

Characters and Unicode

Total characters453331
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowW
3rd rowE
4th rowY
5th rowG
ValueCountFrequency (%)
AH45818
15.3%
E39601
13.2%
AS25326
 
8.4%
J16135
 
5.4%
AN16097
 
5.4%
U15674
 
5.2%
N14983
 
5.0%
AF11455
 
3.8%
AK9697
 
3.2%
AV7958
 
2.7%
Other values (41)97256
32.4%
2021-03-07T20:34:01.867892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ah45818
15.3%
e39601
13.2%
as25326
 
8.4%
j16135
 
5.4%
an16097
 
5.4%
u15674
 
5.2%
n14983
 
5.0%
af11455
 
3.8%
ak9697
 
3.2%
av7958
 
2.7%
Other values (41)97256
32.4%

Most occurring characters

ValueCountFrequency (%)
A163455
36.1%
H47510
 
10.5%
E40059
 
8.8%
S33247
 
7.3%
N31080
 
6.9%
F17005
 
3.8%
J16364
 
3.6%
U16345
 
3.6%
K15961
 
3.5%
V9756
 
2.2%
Other values (15)62549
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter453331
100.0%

Most frequent character per category

ValueCountFrequency (%)
A163455
36.1%
H47510
 
10.5%
E40059
 
8.8%
S33247
 
7.3%
N31080
 
6.9%
F17005
 
3.8%
J16364
 
3.6%
U16345
 
3.6%
K15961
 
3.5%
V9756
 
2.2%
Other values (15)62549
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin453331
100.0%

Most frequent character per script

ValueCountFrequency (%)
A163455
36.1%
H47510
 
10.5%
E40059
 
8.8%
S33247
 
7.3%
N31080
 
6.9%
F17005
 
3.8%
J16364
 
3.6%
U16345
 
3.6%
K15961
 
3.5%
V9756
 
2.2%
Other values (15)62549
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII453331
100.0%

Most frequent character per block

ValueCountFrequency (%)
A163455
36.1%
H47510
 
10.5%
E40059
 
8.8%
S33247
 
7.3%
N31080
 
6.9%
F17005
 
3.8%
J16364
 
3.6%
U16345
 
3.6%
K15961
 
3.5%
V9756
 
2.2%
Other values (15)62549
 
13.8%

cat8
Categorical

HIGH CARDINALITY

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.8 MiB
BM
42380 
AE
24442 
AX
22129 
Y
20864 
H
 
15561
Other values (56)
174624 

Length

Max length2
Median length2
Mean length1.589623333
Min length1

Characters and Unicode

Total characters476887
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ
2nd rowAD
3rd rowBM
4th rowAD
5th rowQ
ValueCountFrequency (%)
BM42380
14.1%
AE24442
 
8.1%
AX22129
 
7.4%
Y20864
 
7.0%
H15561
 
5.2%
S14991
 
5.0%
AD14663
 
4.9%
X14219
 
4.7%
L13573
 
4.5%
AT13216
 
4.4%
Other values (51)103962
34.7%
2021-03-07T20:34:02.296784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bm42380
14.1%
ae24442
 
8.1%
ax22129
 
7.4%
y20864
 
7.0%
h15561
 
5.2%
s14991
 
5.0%
ad14663
 
4.9%
x14219
 
4.7%
l13573
 
4.5%
at13216
 
4.4%
Other values (51)103962
34.7%

Most occurring characters

ValueCountFrequency (%)
A114108
23.9%
B66643
14.0%
M46309
9.7%
X36348
 
7.6%
E24949
 
5.2%
Y21547
 
4.5%
S20018
 
4.2%
N18544
 
3.9%
K18118
 
3.8%
H16855
 
3.5%
Other values (15)93448
19.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter476887
100.0%

Most frequent character per category

ValueCountFrequency (%)
A114108
23.9%
B66643
14.0%
M46309
9.7%
X36348
 
7.6%
E24949
 
5.2%
Y21547
 
4.5%
S20018
 
4.2%
N18544
 
3.9%
K18118
 
3.8%
H16855
 
3.5%
Other values (15)93448
19.6%

Most occurring scripts

ValueCountFrequency (%)
Latin476887
100.0%

Most frequent character per script

ValueCountFrequency (%)
A114108
23.9%
B66643
14.0%
M46309
9.7%
X36348
 
7.6%
E24949
 
5.2%
Y21547
 
4.5%
S20018
 
4.2%
N18544
 
3.9%
K18118
 
3.8%
H16855
 
3.5%
Other values (15)93448
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII476887
100.0%

Most frequent character per block

ValueCountFrequency (%)
A114108
23.9%
B66643
14.0%
M46309
9.7%
X36348
 
7.6%
E24949
 
5.2%
Y21547
 
4.5%
S20018
 
4.2%
N18544
 
3.9%
K18118
 
3.8%
H16855
 
3.5%
Other values (15)93448
19.6%

cat9
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
A
201945 
E
33046 
C
23360 
F
 
14371
J
 
8982
Other values (14)
 
18296

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowF
3rd rowL
4th rowF
5th rowA
ValueCountFrequency (%)
A201945
67.3%
E33046
 
11.0%
C23360
 
7.8%
F14371
 
4.8%
J8982
 
3.0%
I7931
 
2.6%
N4785
 
1.6%
L2957
 
1.0%
R862
 
0.3%
V360
 
0.1%
Other values (9)1401
 
0.5%
2021-03-07T20:34:02.701901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a201945
67.3%
e33046
 
11.0%
c23360
 
7.8%
f14371
 
4.8%
j8982
 
3.0%
i7931
 
2.6%
n4785
 
1.6%
l2957
 
1.0%
r862
 
0.3%
v360
 
0.1%
Other values (9)1401
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A201945
67.3%
E33046
 
11.0%
C23360
 
7.8%
F14371
 
4.8%
J8982
 
3.0%
I7931
 
2.6%
N4785
 
1.6%
L2957
 
1.0%
R862
 
0.3%
V360
 
0.1%
Other values (9)1401
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
A201945
67.3%
E33046
 
11.0%
C23360
 
7.8%
F14371
 
4.8%
J8982
 
3.0%
I7931
 
2.6%
N4785
 
1.6%
L2957
 
1.0%
R862
 
0.3%
V360
 
0.1%
Other values (9)1401
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
A201945
67.3%
E33046
 
11.0%
C23360
 
7.8%
F14371
 
4.8%
J8982
 
3.0%
I7931
 
2.6%
N4785
 
1.6%
L2957
 
1.0%
R862
 
0.3%
V360
 
0.1%
Other values (9)1401
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
A201945
67.3%
E33046
 
11.0%
C23360
 
7.8%
F14371
 
4.8%
J8982
 
3.0%
I7931
 
2.6%
N4785
 
1.6%
L2957
 
1.0%
R862
 
0.3%
V360
 
0.1%
Other values (9)1401
 
0.5%

cat10
Categorical

HIGH CARDINALITY

Distinct299
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 MiB
DJ
31584 
HK
30998 
DP
23679 
GS
 
16619
CR
 
14382
Other values (294)
182738 

Length

Max length2
Median length2
Mean length1.985373333
Min length1

Characters and Unicode

Total characters595612
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowLO
2nd rowHJ
3rd rowDJ
4th rowKV
5th rowDP
ValueCountFrequency (%)
DJ31584
 
10.5%
HK30998
 
10.3%
DP23679
 
7.9%
GS16619
 
5.5%
CR14382
 
4.8%
HX13171
 
4.4%
CK10587
 
3.5%
DC10283
 
3.4%
HQ9580
 
3.2%
MD6817
 
2.3%
Other values (289)132300
44.1%
2021-03-07T20:34:03.125252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dj31584
 
10.5%
hk30998
 
10.3%
dp23679
 
7.9%
gs16619
 
5.5%
cr14382
 
4.8%
hx13171
 
4.4%
ck10587
 
3.5%
dc10283
 
3.4%
hq9580
 
3.2%
md6817
 
2.3%
Other values (289)132300
44.1%

Most occurring characters

ValueCountFrequency (%)
D80920
13.6%
H79548
13.4%
K56288
 
9.5%
C49312
 
8.3%
J44016
 
7.4%
G37382
 
6.3%
L32660
 
5.5%
P25415
 
4.3%
S19727
 
3.3%
M18877
 
3.2%
Other values (15)151467
25.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter595612
100.0%

Most frequent character per category

ValueCountFrequency (%)
D80920
13.6%
H79548
13.4%
K56288
 
9.5%
C49312
 
8.3%
J44016
 
7.4%
G37382
 
6.3%
L32660
 
5.5%
P25415
 
4.3%
S19727
 
3.3%
M18877
 
3.2%
Other values (15)151467
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin595612
100.0%

Most frequent character per script

ValueCountFrequency (%)
D80920
13.6%
H79548
13.4%
K56288
 
9.5%
C49312
 
8.3%
J44016
 
7.4%
G37382
 
6.3%
L32660
 
5.5%
P25415
 
4.3%
S19727
 
3.3%
M18877
 
3.2%
Other values (15)151467
25.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII595612
100.0%

Most frequent character per block

ValueCountFrequency (%)
D80920
13.6%
H79548
13.4%
K56288
 
9.5%
C49312
 
8.3%
J44016
 
7.4%
G37382
 
6.3%
L32660
 
5.5%
P25415
 
4.3%
S19727
 
3.3%
M18877
 
3.2%
Other values (15)151467
25.4%

cat11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
A
258932 
B
41068 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
A258932
86.3%
B41068
 
13.7%
2021-03-07T20:34:03.793127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T20:34:04.000471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a258932
86.3%
b41068
 
13.7%

Most occurring characters

ValueCountFrequency (%)
A258932
86.3%
B41068
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
A258932
86.3%
B41068
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
A258932
86.3%
B41068
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
A258932
86.3%
B41068
 
13.7%

cat12
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
A
257139 
B
42861 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowA
5th rowA
ValueCountFrequency (%)
A257139
85.7%
B42861
 
14.3%
2021-03-07T20:34:04.560620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T20:34:04.810603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a257139
85.7%
b42861
 
14.3%

Most occurring characters

ValueCountFrequency (%)
A257139
85.7%
B42861
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
A257139
85.7%
B42861
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
A257139
85.7%
B42861
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
A257139
85.7%
B42861
 
14.3%

cat13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
A
292712 
B
 
7288

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
A292712
97.6%
B7288
 
2.4%
2021-03-07T20:34:05.491964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T20:34:05.705548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a292712
97.6%
b7288
 
2.4%

Most occurring characters

ValueCountFrequency (%)
A292712
97.6%
B7288
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
A292712
97.6%
B7288
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
A292712
97.6%
B7288
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
A292712
97.6%
B7288
 
2.4%

cat14
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
A
160166 
B
139834 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowA
4th rowA
5th rowB
ValueCountFrequency (%)
A160166
53.4%
B139834
46.6%
2021-03-07T20:34:06.738863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T20:34:06.876660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a160166
53.4%
b139834
46.6%

Most occurring characters

ValueCountFrequency (%)
A160166
53.4%
B139834
46.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
A160166
53.4%
B139834
46.6%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
A160166
53.4%
B139834
46.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
A160166
53.4%
B139834
46.6%

cat15
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
B
203574 
D
83188 
A
 
11072
C
 
2166

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowD
3rd rowB
4th rowB
5th rowB
ValueCountFrequency (%)
B203574
67.9%
D83188
27.7%
A11072
 
3.7%
C2166
 
0.7%
2021-03-07T20:34:07.648494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T20:34:07.772465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
b203574
67.9%
d83188
27.7%
a11072
 
3.7%
c2166
 
0.7%

Most occurring characters

ValueCountFrequency (%)
B203574
67.9%
D83188
27.7%
A11072
 
3.7%
C2166
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
B203574
67.9%
D83188
27.7%
A11072
 
3.7%
C2166
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
B203574
67.9%
D83188
27.7%
A11072
 
3.7%
C2166
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
B203574
67.9%
D83188
27.7%
A11072
 
3.7%
C2166
 
0.7%

cat16
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
D
206906 
B
84541 
C
 
5369
A
 
3184

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowB
3rd rowD
4th rowD
5th rowB
ValueCountFrequency (%)
D206906
69.0%
B84541
28.2%
C5369
 
1.8%
A3184
 
1.1%
2021-03-07T20:34:08.151103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T20:34:08.276225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
d206906
69.0%
b84541
28.2%
c5369
 
1.8%
a3184
 
1.1%

Most occurring characters

ValueCountFrequency (%)
D206906
69.0%
B84541
28.2%
C5369
 
1.8%
A3184
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
D206906
69.0%
B84541
28.2%
C5369
 
1.8%
A3184
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
D206906
69.0%
B84541
28.2%
C5369
 
1.8%
A3184
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
D206906
69.0%
B84541
28.2%
C5369
 
1.8%
A3184
 
1.1%

cat17
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
D
247125 
B
26136 
C
25325 
A
 
1414

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD
ValueCountFrequency (%)
D247125
82.4%
B26136
 
8.7%
C25325
 
8.4%
A1414
 
0.5%
2021-03-07T20:34:08.618259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T20:34:08.740403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
d247125
82.4%
b26136
 
8.7%
c25325
 
8.4%
a1414
 
0.5%

Most occurring characters

ValueCountFrequency (%)
D247125
82.4%
B26136
 
8.7%
C25325
 
8.4%
A1414
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
D247125
82.4%
B26136
 
8.7%
C25325
 
8.4%
A1414
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
D247125
82.4%
B26136
 
8.7%
C25325
 
8.4%
A1414
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
D247125
82.4%
B26136
 
8.7%
C25325
 
8.4%
A1414
 
0.5%

cat18
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
B
255482 
D
 
22394
C
 
21414
A
 
710

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB
ValueCountFrequency (%)
B255482
85.2%
D22394
 
7.5%
C21414
 
7.1%
A710
 
0.2%
2021-03-07T20:34:09.186781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T20:34:09.438140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
b255482
85.2%
d22394
 
7.5%
c21414
 
7.1%
a710
 
0.2%

Most occurring characters

ValueCountFrequency (%)
B255482
85.2%
D22394
 
7.5%
C21414
 
7.1%
A710
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
B255482
85.2%
D22394
 
7.5%
C21414
 
7.1%
A710
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
B255482
85.2%
D22394
 
7.5%
C21414
 
7.1%
A710
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
B255482
85.2%
D22394
 
7.5%
C21414
 
7.1%
A710
 
0.2%

cont0
Real number (ℝ)

Distinct299874
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5047487737
Minimum-0.04956166967
Maximum1.00455918
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:09.894747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.04956166967
5-th percentile0.2059423982
Q10.3449854324
median0.4796503768
Q30.6376572541
95-th percentile0.9172689647
Maximum1.00455918
Range1.05412085
Interquartile range (IQR)0.2926718217

Descriptive statistics

Standard deviation0.2067876275
Coefficient of variation (CV)0.4096842593
Kurtosis-0.2671723398
Mean0.5047487737
Median Absolute Deviation (MAD)0.142632545
Skewness0.4780364331
Sum151424.6321
Variance0.04276112288
MonotocityNot monotonic
2021-03-07T20:34:10.384843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.31569653942
 
< 0.1%
0.55523337532
 
< 0.1%
0.95380238952
 
< 0.1%
0.36668910992
 
< 0.1%
0.50558008972
 
< 0.1%
0.86613939812
 
< 0.1%
0.46057582352
 
< 0.1%
0.37118420142
 
< 0.1%
0.4731199022
 
< 0.1%
0.34355725732
 
< 0.1%
Other values (299864)299980
> 99.9%
ValueCountFrequency (%)
-0.049561669671
< 0.1%
-0.048663105361
< 0.1%
-0.048513204191
< 0.1%
-0.047267404371
< 0.1%
-0.043335903891
< 0.1%
-0.041931001761
< 0.1%
-0.037927938771
< 0.1%
-0.037306044971
< 0.1%
-0.036868517911
< 0.1%
-0.036485544151
< 0.1%
ValueCountFrequency (%)
1.004559181
< 0.1%
0.99784647521
< 0.1%
0.99672995231
< 0.1%
0.9953078041
< 0.1%
0.99500743661
< 0.1%
0.99458883851
< 0.1%
0.99456468541
< 0.1%
0.99411440871
< 0.1%
0.99395780141
< 0.1%
0.99388486311
< 0.1%

cont1
Real number (ℝ≥0)

Distinct299861
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4940732522
Minimum0.08448021242
Maximum1.009958152
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:10.931380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.08448021242
5-th percentile0.1935791165
Q10.3173628997
median0.4631697986
Q30.693992098
95-th percentile0.8301602189
Maximum1.009958152
Range0.9254779396
Interquartile range (IQR)0.3766291984

Descriptive statistics

Standard deviation0.2130365801
Coefficient of variation (CV)0.4311842002
Kurtosis-1.070807048
Mean0.4940732522
Median Absolute Deviation (MAD)0.1688211455
Skewness0.2689466925
Sum148221.9757
Variance0.04538458446
MonotocityNot monotonic
2021-03-07T20:34:11.246690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.74383299492
 
< 0.1%
0.65934768122
 
< 0.1%
0.81417949282
 
< 0.1%
0.54878324632
 
< 0.1%
0.3383455752
 
< 0.1%
0.31607057912
 
< 0.1%
0.68686219562
 
< 0.1%
0.51706000672
 
< 0.1%
0.31322172222
 
< 0.1%
0.24526795772
 
< 0.1%
Other values (299851)299980
> 99.9%
ValueCountFrequency (%)
0.084480212421
< 0.1%
0.088998374081
< 0.1%
0.089018454391
< 0.1%
0.089944314421
< 0.1%
0.091200111211
< 0.1%
0.09233343111
< 0.1%
0.092420008041
< 0.1%
0.093240103051
< 0.1%
0.094191466981
< 0.1%
0.094660041141
< 0.1%
ValueCountFrequency (%)
1.0099581521
< 0.1%
1.0057930031
< 0.1%
1.0054693381
< 0.1%
1.0051271991
< 0.1%
1.0050522271
< 0.1%
1.0027195131
< 0.1%
1.0017125781
< 0.1%
1.0012073641
< 0.1%
1.0008792541
< 0.1%
1.0004813281
< 0.1%

cont2
Real number (ℝ≥0)

Distinct299872
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5166315346
Minimum0.09449347052
Maximum1.01660001
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:11.895499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.09449347052
5-th percentile0.2285475562
Q10.3260473778
median0.4712472407
Q30.704625356
95-th percentile0.8770972953
Maximum1.01660001
Range0.9221065391
Interquartile range (IQR)0.3785779781

Descriptive statistics

Standard deviation0.2148510313
Coefficient of variation (CV)0.4158689837
Kurtosis-1.100728904
Mean0.5166315346
Median Absolute Deviation (MAD)0.1738993448
Skewness0.3171383591
Sum154989.4604
Variance0.04616096564
MonotocityNot monotonic
2021-03-07T20:34:12.154670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44941563242
 
< 0.1%
0.27449177472
 
< 0.1%
0.43422134692
 
< 0.1%
0.54542864682
 
< 0.1%
0.39103564312
 
< 0.1%
0.3395032712
 
< 0.1%
0.33566399952
 
< 0.1%
0.33472330892
 
< 0.1%
0.32665154812
 
< 0.1%
0.32565338082
 
< 0.1%
Other values (299862)299980
> 99.9%
ValueCountFrequency (%)
0.094493470521
< 0.1%
0.09590565881
< 0.1%
0.099276005141
< 0.1%
0.10068316551
< 0.1%
0.10255478381
< 0.1%
0.10422974391
< 0.1%
0.1050892011
< 0.1%
0.10521322751
< 0.1%
0.1061890881
< 0.1%
0.10634505621
< 0.1%
ValueCountFrequency (%)
1.016600011
< 0.1%
1.0089239021
< 0.1%
1.0076714411
< 0.1%
1.0071898321
< 0.1%
1.0057431361
< 0.1%
1.0055151481
< 0.1%
1.005496031
< 0.1%
1.0046552961
< 0.1%
1.0036579921
< 0.1%
1.0029259481
< 0.1%

cont3
Real number (ℝ)

Distinct299818
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.474234771
Minimum-0.04531554409
Maximum0.952186971
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:13.003915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.04531554409
5-th percentile0.1871417841
Q10.2922493538
median0.3875635029
Q30.64494982
95-th percentile0.8407486534
Maximum0.952186971
Range0.997502515
Interquartile range (IQR)0.3527004662

Descriptive statistics

Standard deviation0.2166358179
Coefficient of variation (CV)0.4568113331
Kurtosis-1.193180494
Mean0.474234771
Median Absolute Deviation (MAD)0.1796031588
Skewness0.2229798412
Sum142270.4313
Variance0.04693107762
MonotocityNot monotonic
2021-03-07T20:34:13.278631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.61888365592
 
< 0.1%
0.31708742982
 
< 0.1%
0.5679668342
 
< 0.1%
0.5617030742
 
< 0.1%
0.58298394652
 
< 0.1%
0.55458291162
 
< 0.1%
0.83843948272
 
< 0.1%
0.80650228422
 
< 0.1%
0.54852726652
 
< 0.1%
0.2889973232
 
< 0.1%
Other values (299808)299980
> 99.9%
ValueCountFrequency (%)
-0.045315544091
< 0.1%
-0.044761388581
< 0.1%
-0.031297826541
< 0.1%
-0.030194097761
< 0.1%
-0.029088213861
< 0.1%
-0.028288355211
< 0.1%
-0.028146599361
< 0.1%
-0.026480282961
< 0.1%
-0.023595954311
< 0.1%
-0.02268306761
< 0.1%
ValueCountFrequency (%)
0.9521869711
< 0.1%
0.95101797311
< 0.1%
0.94403726591
< 0.1%
0.94245236421
< 0.1%
0.94182709561
< 0.1%
0.93940428211
< 0.1%
0.93921491081
< 0.1%
0.93784112281
< 0.1%
0.93681186861
< 0.1%
0.93667657331
< 0.1%

cont4
Real number (ℝ≥0)

Distinct299876
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5048478038
Minimum0.1680707771
Maximum0.8585777089
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:14.058585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1680707771
5-th percentile0.2163264717
Q10.2793033597
median0.4798577145
Q30.725779066
95-th percentile0.8117992012
Maximum0.8585777089
Range0.6905069318
Interquartile range (IQR)0.4464757063

Descriptive statistics

Standard deviation0.2274738366
Coefficient of variation (CV)0.4505790356
Kurtosis-1.653381043
Mean0.5048478038
Median Absolute Deviation (MAD)0.2277504662
Skewness0.05670485152
Sum151454.3411
Variance0.05174434631
MonotocityNot monotonic
2021-03-07T20:34:14.586956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.22903375162
 
< 0.1%
0.62385034012
 
< 0.1%
0.79165211492
 
< 0.1%
0.23619753332
 
< 0.1%
0.787238662
 
< 0.1%
0.56449046992
 
< 0.1%
0.28058632362
 
< 0.1%
0.2157857162
 
< 0.1%
0.24906447892
 
< 0.1%
0.78834322252
 
< 0.1%
Other values (299866)299980
> 99.9%
ValueCountFrequency (%)
0.16807077711
< 0.1%
0.16949458731
< 0.1%
0.17050756661
< 0.1%
0.17140318921
< 0.1%
0.17180422661
< 0.1%
0.1721867051
< 0.1%
0.17220610361
< 0.1%
0.17229479011
< 0.1%
0.17292042181
< 0.1%
0.17300851581
< 0.1%
ValueCountFrequency (%)
0.85857770891
< 0.1%
0.85697465741
< 0.1%
0.8567204421
< 0.1%
0.85669889911
< 0.1%
0.85582021791
< 0.1%
0.85530970821
< 0.1%
0.85496319811
< 0.1%
0.85475613091
< 0.1%
0.85444589491
< 0.1%
0.85424204211
< 0.1%

cont5
Real number (ℝ)

Distinct299791
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5022572769
Minimum-0.03637924632
Maximum0.853021701
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:15.154158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.03637924632
5-th percentile0.09089471488
Q10.2764421883
median0.5547683309
Q30.7352250729
95-th percentile0.8006065872
Maximum0.853021701
Range0.8894009474
Interquartile range (IQR)0.4587828846

Descriptive statistics

Standard deviation0.2412431896
Coefficient of variation (CV)0.4803179579
Kurtosis-1.174284111
Mean0.5022572769
Median Absolute Deviation (MAD)0.1949763509
Skewness-0.4183566334
Sum150677.1831
Variance0.05819827652
MonotocityNot monotonic
2021-03-07T20:34:15.384109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.73429286992
 
< 0.1%
0.080427230312
 
< 0.1%
0.15082206122
 
< 0.1%
0.045335153212
 
< 0.1%
0.078095764732
 
< 0.1%
0.73670845262
 
< 0.1%
0.26161794562
 
< 0.1%
0.78860518392
 
< 0.1%
0.48691997542
 
< 0.1%
0.48747029192
 
< 0.1%
Other values (299781)299980
> 99.9%
ValueCountFrequency (%)
-0.036379246321
< 0.1%
-0.035833757851
< 0.1%
-0.033694809161
< 0.1%
-0.033376727361
< 0.1%
-0.032328611831
< 0.1%
-0.030820874421
< 0.1%
-0.029040217041
< 0.1%
-0.026406133641
< 0.1%
-0.020741574721
< 0.1%
-0.020260625981
< 0.1%
ValueCountFrequency (%)
0.8530217011
< 0.1%
0.85300796871
< 0.1%
0.85043106551
< 0.1%
0.8468208341
< 0.1%
0.84641298991
< 0.1%
0.84607384631
< 0.1%
0.84547821031
< 0.1%
0.84481399031
< 0.1%
0.84403759141
< 0.1%
0.84383268881
< 0.1%

cont6
Real number (ℝ≥0)

Distinct299843
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4882287399
Minimum0.005199160834
Maximum0.9665529929
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:15.893988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.005199160834
5-th percentile0.1578387839
Q10.3239139694
median0.5180886272
Q30.608537092
95-th percentile0.8559651257
Maximum0.9665529929
Range0.9613538321
Interquartile range (IQR)0.2846231227

Descriptive statistics

Standard deviation0.211334505
Coefficient of variation (CV)0.4328596163
Kurtosis-0.6454484656
Mean0.4882287399
Median Absolute Deviation (MAD)0.148729993
Skewness0.08538870712
Sum146468.622
Variance0.04466227301
MonotocityNot monotonic
2021-03-07T20:34:16.508420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12414634582
 
< 0.1%
0.45198149982
 
< 0.1%
0.44257128592
 
< 0.1%
0.93735651892
 
< 0.1%
0.63336778022
 
< 0.1%
0.58123479622
 
< 0.1%
0.59774097112
 
< 0.1%
0.63076726432
 
< 0.1%
0.44967657272
 
< 0.1%
0.57471376882
 
< 0.1%
Other values (299833)299980
> 99.9%
ValueCountFrequency (%)
0.0051991608341
< 0.1%
0.0086495895851
< 0.1%
0.010605096761
< 0.1%
0.010675925181
< 0.1%
0.011942307671
< 0.1%
0.01234088751
< 0.1%
0.012494379981
< 0.1%
0.016336172841
< 0.1%
0.016528586951
< 0.1%
0.018138249341
< 0.1%
ValueCountFrequency (%)
0.96655299291
< 0.1%
0.96091208531
< 0.1%
0.96066095761
< 0.1%
0.9602178421
< 0.1%
0.9600715751
< 0.1%
0.96001511081
< 0.1%
0.95996596931
< 0.1%
0.95993722081
< 0.1%
0.95989262261
< 0.1%
0.95969394441
< 0.1%

cont7
Real number (ℝ≥0)

Distinct299880
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5017385734
Minimum0.09090146899
Maximum1.035817821
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:17.606098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.09090146899
5-th percentile0.2352078016
Q10.3530537439
median0.4354039125
Q30.6420225432
95-th percentile0.8888574293
Maximum1.035817821
Range0.9449163525
Interquartile range (IQR)0.2889687993

Descriptive statistics

Standard deviation0.2034959383
Coefficient of variation (CV)0.4055816097
Kurtosis-0.6279595579
Mean0.5017385734
Median Absolute Deviation (MAD)0.1124151217
Skewness0.6520443776
Sum150521.572
Variance0.04141059689
MonotocityNot monotonic
2021-03-07T20:34:18.321323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.43784642852
 
< 0.1%
0.3208236742
 
< 0.1%
0.19923957442
 
< 0.1%
0.83580154742
 
< 0.1%
0.29429129812
 
< 0.1%
0.47121719112
 
< 0.1%
0.32956232562
 
< 0.1%
0.64966356092
 
< 0.1%
0.88574196422
 
< 0.1%
0.95266916312
 
< 0.1%
Other values (299870)299980
> 99.9%
ValueCountFrequency (%)
0.090901468991
< 0.1%
0.092014380911
< 0.1%
0.093056159141
< 0.1%
0.097842480781
< 0.1%
0.098160685581
< 0.1%
0.098988905131
< 0.1%
0.099463797681
< 0.1%
0.10060054091
< 0.1%
0.10136933421
< 0.1%
0.10196773971
< 0.1%
ValueCountFrequency (%)
1.0358178211
< 0.1%
1.0287207521
< 0.1%
1.0274581711
< 0.1%
1.0265436941
< 0.1%
1.0243361831
< 0.1%
1.0234171831
< 0.1%
1.0229131231
< 0.1%
1.0211248061
< 0.1%
1.0206538561
< 0.1%
1.0204973731
< 0.1%

cont8
Real number (ℝ≥0)

Distinct299849
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4880743024
Minimum0.02413894872
Maximum1.055884853
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:20.071543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.02413894872
5-th percentile0.2790362409
Q10.3589145642
median0.4109078486
Q30.6127249987
95-th percentile0.8336314342
Maximum1.055884853
Range1.031745905
Interquartile range (IQR)0.2538104345

Descriptive statistics

Standard deviation0.1790483751
Coefficient of variation (CV)0.3668465523
Kurtosis0.06194393328
Mean0.4880743024
Median Absolute Deviation (MAD)0.08040319509
Skewness0.926890409
Sum146422.2907
Variance0.03205832063
MonotocityNot monotonic
2021-03-07T20:34:20.390652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3464958382
 
< 0.1%
0.34403163572
 
< 0.1%
0.37188513742
 
< 0.1%
0.38130013962
 
< 0.1%
0.37753279972
 
< 0.1%
0.38399111562
 
< 0.1%
0.38210994842
 
< 0.1%
0.33282535872
 
< 0.1%
0.37913059262
 
< 0.1%
0.81976661252
 
< 0.1%
Other values (299839)299980
> 99.9%
ValueCountFrequency (%)
0.024138948721
< 0.1%
0.030979405151
< 0.1%
0.036728337651
< 0.1%
0.036956870241
< 0.1%
0.037255060491
< 0.1%
0.043397520821
< 0.1%
0.044349066961
< 0.1%
0.045832194321
< 0.1%
0.046369391051
< 0.1%
0.049035562641
< 0.1%
ValueCountFrequency (%)
1.0558848531
< 0.1%
1.0542569211
< 0.1%
1.0518655181
< 0.1%
1.0504252561
< 0.1%
1.0493905661
< 0.1%
1.047814311
< 0.1%
1.0457056111
< 0.1%
1.0435068541
< 0.1%
1.0434670321
< 0.1%
1.0434585771
< 0.1%

cont9
Real number (ℝ≥0)

Distinct299859
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4694962058
Minimum0.2148657004
Maximum1.005652336
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:20.854336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.2148657004
5-th percentile0.2539189331
Q10.3099335444
median0.4074767369
Q30.5867929093
95-th percentile0.8299325558
Maximum1.005652336
Range0.7907866356
Interquartile range (IQR)0.2768593649

Descriptive statistics

Standard deviation0.1945162018
Coefficient of variation (CV)0.4143083573
Kurtosis-0.3873001338
Mean0.4694962058
Median Absolute Deviation (MAD)0.1247633209
Skewness0.8092472451
Sum140848.8617
Variance0.03783655276
MonotocityNot monotonic
2021-03-07T20:34:21.372561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27309904822
 
< 0.1%
0.34649936192
 
< 0.1%
0.34201772022
 
< 0.1%
0.29587496072
 
< 0.1%
0.26663506342
 
< 0.1%
0.40697056942
 
< 0.1%
0.80508347142
 
< 0.1%
0.63105259362
 
< 0.1%
0.30035715712
 
< 0.1%
0.3153653722
 
< 0.1%
Other values (299849)299980
> 99.9%
ValueCountFrequency (%)
0.21486570041
< 0.1%
0.21557468221
< 0.1%
0.21785214181
< 0.1%
0.21875171311
< 0.1%
0.21879634311
< 0.1%
0.21917139381
< 0.1%
0.21921817241
< 0.1%
0.22005569211
< 0.1%
0.22031092981
< 0.1%
0.22033476181
< 0.1%
ValueCountFrequency (%)
1.0056523361
< 0.1%
1.0053921011
< 0.1%
1.0050856861
< 0.1%
1.0044538621
< 0.1%
1.0033321431
< 0.1%
1.0028787021
< 0.1%
1.0028047221
< 0.1%
1.0023513971
< 0.1%
1.0023132331
< 0.1%
1.0003306881
< 0.1%

cont10
Real number (ℝ≥0)

Distinct299823
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.508229662
Minimum0.0977887102
Maximum1.01133078
Zeros0
Zeros (%)0.0%
Memory size2.3 MiB
2021-03-07T20:34:21.926292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0977887102
5-th percentile0.293340502
Q10.3683123067
median0.4465762453
Q30.5819566617
95-th percentile0.9501228714
Maximum1.01133078
Range0.9135420696
Interquartile range (IQR)0.213644355

Descriptive statistics

Standard deviation0.2033931051
Coefficient of variation (CV)0.4001992018
Kurtosis0.1206544893
Mean0.508229662
Median Absolute Deviation (MAD)0.09879517209
Skewness1.08777537
Sum152468.8986
Variance0.04136875518
MonotocityNot monotonic
2021-03-07T20:34:22.189215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56662031262
 
< 0.1%
0.51864570212
 
< 0.1%
0.95267214122
 
< 0.1%
0.50888161522
 
< 0.1%
0.40452457172
 
< 0.1%
0.30130592262
 
< 0.1%
0.95102646492
 
< 0.1%
0.51546087642
 
< 0.1%
0.55106302122
 
< 0.1%
0.26301730322
 
< 0.1%
Other values (299813)299980
> 99.9%
ValueCountFrequency (%)
0.09778871021
< 0.1%
0.10620836161
< 0.1%
0.10904029941
< 0.1%
0.1095410521
< 0.1%
0.11773448881
< 0.1%
0.12671085921
< 0.1%
0.127852811
< 0.1%
0.12826453991
< 0.1%
0.13031745231
< 0.1%
0.1320487741
< 0.1%
ValueCountFrequency (%)
1.011330781
< 0.1%
1.0080596551
< 0.1%
1.007003461
< 0.1%
1.0066065911
< 0.1%
1.0065449721
< 0.1%
1.0064794721
< 0.1%
1.0064639131
< 0.1%
1.0055617391
< 0.1%
1.0039870361
< 0.1%
1.0038743591
< 0.1%

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
0
220539 
1
79461 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1
ValueCountFrequency (%)
0220539
73.5%
179461
 
26.5%
2021-03-07T20:34:22.662854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T20:34:22.836622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0220539
73.5%
179461
 
26.5%

Most occurring characters

ValueCountFrequency (%)
0220539
73.5%
179461
 
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number300000
100.0%

Most frequent character per category

ValueCountFrequency (%)
0220539
73.5%
179461
 
26.5%

Most occurring scripts

ValueCountFrequency (%)
Common300000
100.0%

Most frequent character per script

ValueCountFrequency (%)
0220539
73.5%
179461
 
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII300000
100.0%

Most frequent character per block

ValueCountFrequency (%)
0220539
73.5%
179461
 
26.5%

Interactions

2021-03-07T20:32:36.352373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:37.029850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:37.726083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:38.151429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:39.358232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:39.834122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:40.307471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:40.726908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:41.036271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:41.313130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:41.689240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:42.108410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:42.395315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:42.640346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:42.886005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:43.143941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:43.515510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:43.830040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:44.419492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:45.037431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:45.924011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:46.512706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:47.374352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:48.381234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:48.992294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:49.678712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:50.244098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:50.826164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:51.317165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:51.952871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:52.692979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:53.380452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:53.991898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:54.567889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:55.480125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:56.426753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:57.587997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:58.996545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:32:59.963330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:00.481242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:00.879828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:01.119148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:01.355534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:01.603968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:01.833434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:02.077638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:02.323288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:02.574141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:02.846974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:03.232117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:03.622454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:03.983940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:04.537143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:05.010122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:05.405952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:05.801651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:06.260623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:06.712134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:07.346995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:07.778566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:08.080657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:08.408003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:08.808292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:09.124696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:09.423803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:09.723347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:10.095843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:11.333622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:11.913273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:12.495589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:12.951072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:13.428152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:13.780191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:14.049753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:14.386903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:14.667496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:14.956004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:15.398158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:15.669991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:15.958692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:16.347472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:16.816529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:17.485131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:18.456957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:19.196859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:20.445954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:20.952748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:21.238837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:21.568822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:21.833304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:22.265374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:22.786704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:23.305605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:24.289488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:25.578453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:26.274186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:26.992946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:27.530416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:28.064275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:28.346688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:28.855156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:29.141243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:29.572154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:29.849871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:30.145391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:30.540779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:30.949876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:31.230656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:31.556616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:32.018401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:32.368975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:32.726560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:33.068160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:33.499634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:33.783759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:34.088808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:34.399066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:35.088961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:35.399221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:35.739612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:36.074047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:36.443497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:37.436287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:38.213642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:39.286492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:40.094090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:40.931282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:41.211019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:41.474365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:41.737440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:42.005447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-07T20:33:42.419273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-07T20:34:22.984889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-07T20:34:23.854660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-07T20:34:24.749615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-07T20:34:26.176807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-07T20:34:27.062230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-07T20:33:47.050043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-07T20:33:51.649410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idcat0cat1cat2cat3cat4cat5cat6cat7cat8cat9cat10cat11cat12cat13cat14cat15cat16cat17cat18cont0cont1cont2cont3cont4cont5cont6cont7cont8cont9cont10target
00AIABBBIASQALOAAAABDDB0.6298580.8553490.7594390.7955490.6819170.6216720.5921840.7919210.8152540.9650060.6659150
11AIAAEBIKWADFHJABABDBDB0.3707270.3289290.3863850.5413660.3889820.3577780.6000440.4087010.3993530.9274060.4937290
22AKAAEBIAEBMLDJABAABDDB0.5022720.3227490.3432550.6163520.7936870.5528770.3521130.3888350.4123030.2926960.5494520
33AKACEBIAYADFKVAAAABDDB0.9342420.7076630.8311470.8078070.8000320.6191470.2217890.8976170.6336690.7603180.9342420
44AIGBEBICGQADPAAABBBDB0.2544270.2745140.3388180.2773080.6105780.1282910.5787640.2791670.3511030.3570840.3289601
57AICAEBICAVYCGEAAAABDDB0.4804560.4130380.5247600.5800850.3014980.2610550.1939880.9356880.6858680.2776940.4281150
610AAGAHBIASADEHQAAABDDDB0.7990310.7078400.6784660.7471250.6782120.5536090.2817190.7687820.6885800.3206990.6422560
712AFAAIBICAFAGAHCAAAADBCC0.4926810.9667190.9435450.2097440.6705770.7726500.8457510.5073200.9272040.6057550.4541551
813BIAAEBICAKAEEEKAAAABDDB0.6455190.6647180.6351950.6722640.5611270.4198730.5085500.6732670.3561030.4715190.4995540
914ALABEBICAFAXADPBABBDBDC0.4502480.3028960.3976220.5060960.2006730.7341450.5216730.3655710.4160940.5847990.4471491

Last rows

idcat0cat1cat2cat3cat4cat5cat6cat7cat8cat9cat10cat11cat12cat13cat14cat15cat16cat17cat18cont0cont1cont2cont3cont4cont5cont6cont7cont8cont9cont10target
299990499988AGABFBIAANBMALOAABBDBCB0.3509740.2105150.2386120.2694380.5929670.1477100.7212380.2641550.6837300.2656480.4097641
299991499989AKAAEBICAWASECRABAABDDB0.3529180.3689060.3098470.5971380.3652070.4893610.5246140.2504190.4198440.4545390.3125080
299992499990AHADHBIAAFBDAHCAAABDDBB0.5025330.8598020.9379450.5632180.6683290.6182280.6194210.7935990.6850940.9475350.4930941
299993499991AIAAFBIAEADALFAAAABDDB0.9330870.6222050.7474510.6815400.7887050.2511880.3407670.8563330.6289440.7041780.9359090
299994499992AOCAFBIAKBNAGSBAABBDDB0.5019730.3923570.4585360.2549060.2189390.2728910.9462790.4531620.3351830.2599490.4709630
299995499993ANFAEBUAASKAHGAAABDBDB0.6817000.5007300.6624280.6719270.3905660.1458400.2627670.5142480.5193400.6174360.6880070
299996499995AKAAGBIAKAEEHKABABBDDB0.4892260.7906640.8216570.6203560.3848910.7358790.5477310.7266530.4705750.2757430.6389390
299997499996AGMAHBICLFAHCBAABDBDD0.4878820.5223470.4070370.2324360.8324820.8106630.5969390.3088210.3739970.5180240.4521441
299998499997BHADBBIAAAAXABFAAAABADA0.3319000.8128910.8080450.6307080.3468980.7351470.5634880.6098360.6804300.3184530.3358220
299999499999AFCAEBICAVSALMAAAABDDB0.8226000.8197350.7754510.8486960.8193770.3554670.2181530.9688560.8236550.3305150.9725690